改进人员重新识别质量的方法用于实际使用
Timur Mamedov1,2, Denis Kuplyakov1,2, Anton Konushin1,3
1Faculty of Computational Mathematics and Cybernetics, Moscow State University, 119991 Moscow, Russia.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究介绍了一个过器模块和一个自我监督的预培训策略,以提高人重新识别 (Re-ID) 的表现. 这些方法在不显著增加计算负载的情况下提高了准确性,使Re-ID在现实应用中变得更加实用.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 个人重新识别 (Re-ID) 对于监控和安全至关重要.
- 现有的Re-ID算法由于复杂性和输入数据差,经常面临实际限制.
- 需要有效和强大的Re-ID解决方案,适用于现实世界的场景.
研究的目的:
- 提出改善个人Re-ID质量的实际方法,而不会增加计算复杂性.
- 引入一种新的过器模块,用于在Re-ID系统中预先过输入数据.
- 开发自动化数据收集策略,用于自我监督的预训,以增强神经网络的通用性.
主要方法:
- 在Re-ID分析之前开发一个过器模块来预处理图像.
- 实施完全自动化的数据收集策略,用于监控摄像头的录像.
- 使用收集的数据进行神经网络概括的自我监督预训的应用.
主要成果:
- 过器模块在市场-1501数据集上提高了基线Re-ID性能2.6% (排名1) 和3.4% (mAP).
- 在DukeMTMC-reID数据集上,自我监督的预训提高了1.0%的跨域上体Re-ID (排名1和mAP).
- 提出的方法提供了实用改进,最小的计算开销.
结论:
- 拟议的过器模块和自我监督的预培训策略有效地提高了个人的 Re-ID 性能.
- 这些方法解决了Re-ID的实际挑战,提高了稳定性和准确性.
- 这项研究有助于为智能城市监控提供更有效,更可靠的个人重新识别系统.
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